Doctoral Student

Pisa , Toscana, Italy

Type: n/a

Category: Healthcare & Pharma

Requirements: English
Company: IIT-CNR
Region: Pisa , Toscana

A fully funded 3-year Ph.D. position in Network Intelligence in 6G networks within the Italian National PhD Program in Artificial Intelligence is sponsored by the Ubiquitous Internet (UI) research group of IIT-CNR. We are looking for a highly motivated PhD candidate with a strong academic background to join our research team and work on this Ph.D. topic under our supervision.

This scouting notice aims to raise awareness of the upcoming Ph.D. admission call that will be officially opened by the University of Pisa in June.

Interested candidates should send the following documents in a single PDF file:

• A detailed CV
• A list of publications (if any), or a summary of relevant research experience
• Transcripts (Bachelors and Masters degrees)

Start of the Ph.D. course: November 1st, 2025.

Workplace: Pisa, Italy

More infromation about the PhD program and the research group:

https://www.phd-ai.it/en/359-2/

https://ui.iit.cnr.it/en/

Background

One of the primary objectives of future 6G networks is to enable AI-driven intelligence across all layers. AI will be both a service and a native feature within the 6G communication system, making 6G an end-to-end (E2E) AI-powered ecosystem that supports intelligent services and applications. As network complexity escalates with the prolifeartion of diverse devices, incrising data volumes, and heterogenous protocols, and the requirements of emerging applications become more demanding, 6G must leverage E2E AI and ML models to improve network optimization and automate various aspects of network management. This will enable predictive analysis, proactive network management, and dynamic performance alignment to meet diverse E2E demands.

Topic description

The research challenges addressed by this PhD include, but are not limited to::

• the desig of new solutions that leverage recent breakthroughs in generative artificial intelligence (GAI) to support closed-loop network intelligence across the entire E2E network architecture, effectively managing conflicts among multiple decision-making agents operating in different network domains or segments;
• the development of robust mechanisms for interpreting network intelligence decisions and outcomes, utilizing explainable AI (XAI) models to enhance transparency and accountablity.

Candidate profile

The candidate must have (or be about to obtain) a Masters degree (or equivalent) in Computer Science, Computer Engineering, Telecommunications Engineering, or a closely related field. An excellent academic record is required, with outstanding marks in subjects such as networking, optimization, and artificial intelligence.

Technical Skills

The following skills are an advantage for the candidate:

• Experience or familiarity with ML/AI techniques applied to networking.
• Knowledge of general networking principles and mobile network architecture
• Proficiency in programming (Python, C++, or similar languages).

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